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Network in Network Imagenet Model
Raw
readme.md

Info

name: Network in Network Imagenet Model

caffemodel: nin_imagenet.caffemodel

caffemodel_url: https://www.dropbox.com/s/cphemjekve3d80n/nin_imagenet.caffemodel?dl=1 license: BSD

caffe_commit: pull request yet to be merged

gist_id: d802a5849de39225bcc6

Descriptions

This model is a 4 layer Network in Network model trained on imagenet dataset.

Thanks to the replacement of fully connected layer with a global average pooling layer, this model has greatly reduced parameters, which results in a snapshot of size 29MB, compared to AlexNet which is about 230MB, it is one eighth the size.

The top 1 performance of this model on validation set is 59.36%, which is slightly better than AlexNet. (Using the average of 10 crops, (4 + 1 center) * 2 mirror, should obtain a bit higher accuracy.)

The training time of the model is also greatly reduced compared to AlexNet because of the faster convergence. It takes 4-5 days to train on a GTX Titan.

License

BSD

Raw
solver.prototxt
net: "models/nin_imagenet/train_val.prototxt"
test_iter: 1000
test_interval: 1000
base_lr: 0.01
lr_policy: "step"
gamma: 0.1
stepsize: 200000
display: 20
max_iter: 450000
momentum: 0.9
weight_decay: 0.0005
snapshot: 10000
snapshot_prefix: "models/nin_imagenet/nin_imagenet_train"
solver_mode: GPU
Raw
train_val.prototxt
name: "nin_imagenet"
layers {
top: "data"
top: "label"
name: "data"
type: DATA
data_param {
source: "/home/linmin/IMAGENET-LMDB/imagenet-train-lmdb"
backend: LMDB
batch_size: 64
}
transform_param {
crop_size: 224
mirror: true
mean_file: "/home/linmin/IMAGENET-LMDB/imagenet-train-mean"
}
include: { phase: TRAIN }
}
layers {
top: "data"
top: "label"
name: "data"
type: DATA
data_param {
source: "/home/linmin/IMAGENET-LMDB/imagenet-val-lmdb"
backend: LMDB
batch_size: 89
}
transform_param {
crop_size: 224
mirror: false
mean_file: "/home/linmin/IMAGENET-LMDB/imagenet-train-mean"
}
include: { phase: TEST }
}
layers {
bottom: "data"
top: "conv1"
name: "conv1"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
mean: 0
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layers {
bottom: "conv1"
top: "conv1"
name: "relu0"
type: RELU
}
layers {
bottom: "conv1"
top: "cccp1"
name: "cccp1"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 96
kernel_size: 1
stride: 1
weight_filler {
type: "gaussian"
mean: 0
std: 0.05
}
bias_filler {
type: "constant"
value: 0
}
}
}
layers {
bottom: "cccp1"
top: "cccp1"
name: "relu1"
type: RELU
}
layers {
bottom: "cccp1"
top: "cccp2"
name: "cccp2"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 96
kernel_size: 1
stride: 1
weight_filler {
type: "gaussian"
mean: 0
std: 0.05
}
bias_filler {
type: "constant"
value: 0
}
}
}
layers {
bottom: "cccp2"
top: "cccp2"
name: "relu2"
type: RELU
}
layers {
bottom: "cccp2"
top: "pool0"
name: "pool0"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
bottom: "pool0"
top: "conv2"
name: "conv2"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
stride: 1
weight_filler {
type: "gaussian"
mean: 0
std: 0.05
}
bias_filler {
type: "constant"
value: 0
}
}
}
layers {
bottom: "conv2"
top: "conv2"
name: "relu3"
type: RELU
}
layers {
bottom: "conv2"
top: "cccp3"
name: "cccp3"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 256
kernel_size: 1
stride: 1
weight_filler {
type: "gaussian"
mean: 0
std: 0.05
}
bias_filler {
type: "constant"
value: 0
}
}
}
layers {
bottom: "cccp3"
top: "cccp3"
name: "relu5"
type: RELU
}
layers {
bottom: "cccp3"
top: "cccp4"
name: "cccp4"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 256
kernel_size: 1
stride: 1
weight_filler {
type: "gaussian"
mean: 0
std: 0.05
}
bias_filler {
type: "constant"
value: 0
}
}
}
layers {
bottom: "cccp4"
top: "cccp4"
name: "relu6"
type: RELU
}
layers {
bottom: "cccp4"
top: "pool2"
name: "pool2"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
bottom: "pool2"
top: "conv3"
name: "conv3"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "gaussian"
mean: 0
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layers {
bottom: "conv3"
top: "conv3"
name: "relu7"
type: RELU
}
layers {
bottom: "conv3"
top: "cccp5"
name: "cccp5"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 384
kernel_size: 1
stride: 1
weight_filler {
type: "gaussian"
mean: 0
std: 0.05
}
bias_filler {
type: "constant"
value: 0
}
}
}
layers {
bottom: "cccp5"
top: "cccp5"
name: "relu8"
type: RELU
}
layers {
bottom: "cccp5"
top: "cccp6"
name: "cccp6"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 384
kernel_size: 1
stride: 1
weight_filler {
type: "gaussian"
mean: 0
std: 0.05
}
bias_filler {
type: "constant"
value: 0
}
}
}
layers {
bottom: "cccp6"
top: "cccp6"
name: "relu9"
type: RELU
}
layers {
bottom: "cccp6"
top: "pool3"
name: "pool3"
type: POOLING
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layers {
bottom: "pool3"
top: "pool3"
name: "drop"
type: DROPOUT
dropout_param {
dropout_ratio: 0.5
}
}
layers {
bottom: "pool3"
top: "conv4"
name: "conv4-1024"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 1024
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "gaussian"
mean: 0
std: 0.05
}
bias_filler {
type: "constant"
value: 0
}
}
}
layers {
bottom: "conv4"
top: "conv4"
name: "relu10"
type: RELU
}
layers {
bottom: "conv4"
top: "cccp7"
name: "cccp7-1024"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 1024
kernel_size: 1
stride: 1
weight_filler {
type: "gaussian"
mean: 0
std: 0.05
}
bias_filler {
type: "constant"
value: 0
}
}
}
layers {
bottom: "cccp7"
top: "cccp7"
name: "relu11"
type: RELU
}
layers {
bottom: "cccp7"
top: "cccp8"
name: "cccp8-1024"
type: CONVOLUTION
blobs_lr: 1
blobs_lr: 2
weight_decay: 1
weight_decay: 0
convolution_param {
num_output: 1000
kernel_size: 1
stride: 1
weight_filler {
type: "gaussian"
mean: 0
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layers {
bottom: "cccp8"
top: "cccp8"
name: "relu12"
type: RELU
}
layers {
bottom: "cccp8"
top: "pool4"
name: "pool4"
type: POOLING
pooling_param {
pool: AVE
kernel_size: 6
stride: 1
}
}
layers {
name: "accuracy"
type: ACCURACY
bottom: "pool4"
bottom: "label"
top: "accuracy"
include: { phase: TEST }
}
layers {
bottom: "pool4"
bottom: "label"
name: "loss"
type: SOFTMAX_LOSS
include: { phase: TRAIN }
}
@mrgloom
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mrgloom commented Nov 28, 2016

Here is working example
https://github.com/mrgloom/kaggle-dogs-vs-cats-solution/tree/master/learning_from_scratch/Models/NetworkInNetwork

@moyix
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moyix commented Oct 9, 2017

Hi @mavenlin,

I noticed that the SHA1 of the caffe model does not match what's listed here (the SHA1 listed here is 8e89c8fcd46e02780e16c867a5308e7bb7af0803 but the SHA1 of the downloaded model is 2794deb2aada04f667894b7d6d929371b4689ea9). Maybe this should be fixed so that people can be sure their download was successful and they're getting the correct model?

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